Many industries, such as the hydrocarbon industry, gather and utilize a wide variety of data collected from different signals from different sensors. The data often needs to be stored and/or transmitted efficiently. Breaking data into smaller, homogeneous segments has been used extensively to compress a variety of data. These techniques, however, generally compress the entire dataset as a block. Those that claim to be real-time generally work by buffering a large amount of data. This data is then compressed as a block, before it is transmitted or stored.
In many industries, there are more and better sensors providing more detailed information that must be transmitted, processed, acted upon and/or the like. In information technology, cloud computing, satellite transmissions and/or the like, it is often bandwidth that is the limiting factor on data transmission and/or processing. Merely by way of example, in the hydrocarbon industry, there are ever more and better sensors for sensing data related to the exploration, extraction, production and/or transportation of the hydrocarbons. To better handle the storage and transmission of data gathered from sensors—such as in the hydrocarbon industry the sensors related to the exploration, extraction, production and/or transportation of the hydrocarbons—the sensed data associated with the processes needs to be effectively and efficiently handled.